CN117390572A - Vacuum anomaly monitoring system for lamination - Google Patents
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Abstract
The invention relates to the technical field of vacuum anomaly monitoring, and discloses a bonded vacuum anomaly monitoring system, which comprises: the acquisition module is used for acquiring vacuum data of a vacuum cavity of the curved surface vacuum laminating machine; the vacuum data comprise an upper cavity vacuum value, a lower cavity vacuum value and an opening and closing position vacuum value; the analysis module is used for analyzing the curved surface vacuum laminating machine based on the vacuum data to obtain a vacuum reflection value; the processing module is used for calculating and processing to obtain a vacuum anomaly ratio based on the vacuum reflection value; wherein the vacuum anomaly ratio comprises a spatial vacuum anomaly ratio and a temporal vacuum anomaly ratio; the monitoring submodule is used for judging the working state of the curved surface vacuum laminating machine according to the vacuum abnormal ratio to obtain an operation state signal; the invention realizes real-time monitoring of the curved surface vacuum laminating machine and ensures the quality of laminating work of the curved surface vacuum laminating machine; and based on the operation abnormal signal, further judging the abnormal reason to obtain the reason for generating the fault, thereby being convenient for maintenance.
Description
Technical Field
The invention relates to the technical field of vacuum anomaly monitoring, in particular to a bonded vacuum anomaly monitoring system.
Background
Chinese patent CN110825003a discloses a vacuum equipment monitoring control system, which comprises an MCU, a mobile terminal, a cloud server, an equipment sensor module, an environmental sensor module, a communication module, a comparator, an alarm and a fault handling module; the MCU is in communication connection with the cloud server through the communication module, fault early warning information is stored in the fault processing module, the cloud server is in communication connection with the mobile terminal, the alarm and the fault processing module are respectively connected with the MCU, the equipment sensor module and the environment sensor module are connected with the MCU through the comparator, the equipment sensor module detects equipment operation state parameters through the sensor, and the environment sensor module detects environment parameters of an equipment operation surrounding environment through the sensor;
in the prior art, in the curved surface laminating process, abnormal conditions are more complicated in the vacuum degree of the laminating process, the chamber is likely to age after long-time use, the chamber fault is caused to influence the laminating quality, the vacuum generating equipment is also likely to be used for a long time, the abnormal vacuum condition during laminating is also caused, and the vacuum monitoring mode is that: a technician judges the abnormality of the bonding process by observing the vacuum gauge, so that the abnormality has larger error and the accuracy of the bonding process is affected; and the fault problem can not be monitored and judged according to the vacuum data.
Disclosure of Invention
The invention aims to provide a bonded vacuum anomaly monitoring system, which solves the following technical problems: a technician judges the abnormality of the bonding process by observing the vacuum gauge, so that the abnormality has larger error and the accuracy of the bonding process is affected; and the fault problem can not be monitored and judged according to the vacuum data.
The aim of the invention can be achieved by the following technical scheme:
a conformable vacuum anomaly monitoring system comprising:
the acquisition module is used for acquiring vacuum data of a vacuum cavity of the curved surface vacuum laminating machine; the vacuum data comprise an upper cavity vacuum value, a lower cavity vacuum value and an opening and closing position vacuum value;
the analysis module is used for analyzing the curved surface vacuum laminating machine based on the vacuum data to obtain a vacuum reflection value; wherein the vacuum reflected values include a spatial vacuum reflected value ZPk and a temporal vacuum reflected value ZPt;
the processing module is used for calculating and processing to obtain a vacuum anomaly ratio based on the vacuum reflection value; wherein the vacuum anomaly ratio comprises a space vacuum anomaly ratio Bk and a time vacuum anomaly ratio Bt; the space vacuum anomaly ratio Bk passes through a formulaCalculated, the time vacuum abnormality ratio Bt is calculated by the formula +.>Calculating to obtain ZPky as a preset space vacuum reflecting value and ZPty as a preset time vacuum reflecting value;
the abnormality monitoring module comprises a monitoring sub-module and an abnormality judging sub-module; the monitoring submodule is used for judging the working state of the curved surface vacuum laminating machine according to the vacuum abnormal ratio to obtain an operation state signal; wherein the operation state signal includes an operation normal signal and an operation abnormal signal;
and the abnormality judging sub-module is used for generating a mutual pre-influence signal if the space vacuum abnormality ratio Bk is larger than or equal to the space vacuum abnormality ratio threshold value and the time vacuum abnormality ratio Bt is larger than or equal to the time vacuum abnormality ratio threshold value based on the operation abnormality signal.
As a further scheme of the invention: the spatial vacuum reflected value is obtained as follows:
firstly, obtaining a plurality of opening and closing vacuum values, and adding the plurality of opening and closing vacuum values to obtain an average value, so as to obtain an opening and closing vacuum average value;
then, respectively carrying out difference calculation on the vacuum average value at the opening and closing position and the upper cavity vacuum value and the lower cavity vacuum value to obtain an upper vacuum difference value and a lower vacuum difference value;
meanwhile, calculating the difference value of the vacuum values of the adjacent opening and closing positions to obtain a first vacuum difference value, and adding and summing all the first vacuum difference values to obtain an opening and closing vacuum difference value;
and adding and summing the obtained upper vacuum difference value, lower vacuum difference value and opening and closing vacuum difference value to obtain a space vacuum reflection value ZPk.
As a further scheme of the invention: the acquisition process of the time vacuum reflected value is as follows:
adding and summing the upper cavity vacuum value, the lower cavity vacuum value and the vacuum average value at the opening and closing position to obtain an average value, and obtaining a cavity vacuum average value;
setting an analysis time period, performing difference calculation on the cavity vacuum average values at adjacent moments to obtain second vacuum difference values, and adding and summing all the second vacuum difference values to obtain a time vacuum reflection value ZPt.
As a further scheme of the invention: the duration value of the analysis time period is T, the end of the analysis time period is the time obtained by the space vacuum reflection value ZPk, and the start of the analysis period is the time obtained by the space vacuum reflection value ZPk minus the duration value of the analysis time period is T.
As a further scheme of the invention: if the space vacuum anomaly ratio Bk is smaller than the space vacuum anomaly ratio threshold value, generating a normal operation signal when the time vacuum anomaly ratio Bt is smaller than the time vacuum anomaly ratio threshold value;
otherwise, an operation anomaly signal is generated.
As a further scheme of the invention: if the obtained space vacuum anomaly ratio Bk is larger than or equal to a space vacuum anomaly ratio threshold value, and the time vacuum anomaly ratio Bt is smaller than a time vacuum anomaly ratio threshold value, generating a space vacuum degree influence signal;
if the obtained space vacuum anomaly ratio Bk is smaller than the space vacuum anomaly ratio threshold value, and the time vacuum anomaly ratio Bt is larger than or equal to the time vacuum anomaly ratio threshold value, generating a time vacuum degree influence signal.
As a further scheme of the invention: further comprises:
and a confirmation module: when the mutual pre-influence signals are obtained, a two-dimensional coordinate system is constructed by taking the starting point of an analysis time period as the origin of an X axis, the duration of the analysis time period as the X axis and the vacuum difference as the Y axis;
substituting the first vacuum difference value and the second vacuum difference value into a two-dimensional coordinate system according to the duration of the corresponding analysis time period, and respectively drawing to obtain a first vacuum difference curve and a second vacuum difference curve;
respectively obtaining all peak points and trough points in a first vacuum difference curve and a second vacuum difference curve, marking the peak points and the trough points as a first curve peak point, a first curve trough point, a second curve peak point and a second curve trough point, and counting the sum GBZ of the numbers of all the peak points and the trough points;
the first curve crest point and the second curve crest point are subjected to coincidence comparison, the first curve trough point and the second curve trough point are subjected to coincidence comparison, a coincidence deviation period is set, if the time difference value between the first curve crest point and the second curve crest point is within the coincidence deviation period, the first curve crest point and the second curve trough point are marked as crest coincidence points, and if the time difference value between the first curve trough point and the second curve trough point is within the coincidence deviation period, the first curve trough point and the second curve trough point are marked as trough coincidence points, and the sum GBC of the numbers of all the crest coincidence points and the trough coincidence points is counted;
by the formulaAnd calculating to obtain a resonance appearance value ZZB.
As a further scheme of the invention: the vacuum difference values include a first vacuum difference value and a second vacuum difference value.
As a further scheme of the invention: and generating an opening and closing influence signal when the resonance representation value ZZZB is larger than or equal to the resonance representation threshold value.
As a further scheme of the invention: and if the resonance expression value ZZB is smaller than the resonance expression threshold value, generating a space vacuum degree and time vacuum degree composite influence signal.
The invention has the beneficial effects that:
according to the invention, through the acquisition module, the vacuum data of the vacuum cavity of the curved surface vacuum laminating machine are obtained; the analysis module is used for analyzing the curved surface vacuum laminating machine based on the vacuum data to obtain a vacuum reflection value; the processing module is used for calculating and processing to obtain a vacuum anomaly ratio based on the vacuum reflection value; the abnormality monitoring module comprises a monitoring sub-module and an abnormality judging sub-module; the monitoring submodule judges the working state of the curved surface vacuum laminating machine according to the vacuum abnormal ratio to obtain an operation state signal, and the abnormality judging submodule further analyzes and judges according to the operation abnormality signal; according to the embodiment of the invention, the working operation state of the curved surface vacuum laminating machine is judged by starting from the vacuum data of the vacuum cavity of the curved surface vacuum laminating machine and performing data processing analysis in the space and time dimensions, so that the real-time monitoring of the curved surface vacuum laminating machine is realized, and the laminating quality of the curved surface vacuum laminating machine is ensured; and further judging the abnormal reasons based on the operation abnormal signals to obtain the reasons for generating faults, so that the technical staff can conveniently overhaul the faults.
Drawings
The invention is further described below with reference to the accompanying drawings.
FIG. 1 is a system block diagram of an anomaly monitoring system in accordance with embodiment 1 of the present invention;
FIG. 2 is a system block diagram of an anomaly monitoring module in embodiment 1 of the present invention;
FIG. 3 is a system block diagram of an anomaly monitoring system in accordance with embodiment 2 of the present invention;
fig. 4 is a flow chart of the abnormality monitoring system in embodiment 3 of the present invention.
Description of the embodiments
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1
Referring to fig. 1 and 2, the present invention is a vacuum anomaly monitoring system for bonding, comprising:
and the acquisition module acquires vacuum data of the vacuum cavity of the curved surface vacuum laminating machine.
The curved surface vacuum laminating machine comprises a vacuum cavity, a vacuum sensor and a vacuum sensor, wherein the vacuum cavity of the curved surface vacuum laminating machine is formed by opening and closing two sealing cover plates, the vacuum sensors are respectively arranged on the side walls of the two cover plates, which are far away from each other, and the vacuum sensors are respectively arranged on the side surfaces of the opening and closing positions of the two sealing cover plates.
The vacuum data comprises an upper cavity vacuum value, a lower cavity vacuum value and an opening and closing position vacuum value.
In some embodiments, first, vacuum sensors are respectively arranged on the side walls of the two cover plates of the curved surface vacuum laminating machine, which are far away from each other, so as to obtain the upper cavity vacuum value and the lower cavity vacuum value.
And each side surface of the opening and closing positions of the two sealing cover plates is respectively provided with a vacuum sensor, so that a plurality of vacuum values of the opening and closing positions are obtained.
And the analysis module is used for analyzing the curved surface vacuum laminating machine based on the vacuum data to obtain a vacuum reflection value.
Wherein the vacuum reflected values include a spatial vacuum reflected value ZPk and a temporal vacuum reflected value ZPt.
In some embodiments, the spatial vacuum reflection value is obtained as follows:
firstly, a plurality of opening and closing vacuum values are obtained, and the opening and closing vacuum values are added to obtain an average value, so that the opening and closing vacuum average value is obtained.
And then, respectively carrying out difference calculation on the vacuum average value at the opening and closing position and the upper cavity vacuum value and the lower cavity vacuum value to obtain an upper vacuum difference value and a lower vacuum difference value.
And meanwhile, calculating the difference value of the vacuum values at the adjacent opening and closing positions to obtain a first vacuum difference value, and adding and summing all the first vacuum difference values to obtain an opening and closing vacuum difference value.
And adding and summing the obtained upper vacuum difference value, lower vacuum difference value and opening and closing vacuum difference value to obtain a space vacuum reflection value ZPk.
The acquisition process of the time vacuum reflected value is as follows:
and adding and summing the upper cavity vacuum value, the lower cavity vacuum value and the vacuum average value at the opening and closing position to obtain an average value, and obtaining the cavity vacuum average value.
Setting an analysis time period (the time length value of the analysis time period is T, the end point of the analysis time period is the time obtained by the space vacuum reflecting value ZPk, the time length value of the analysis time period is subtracted from the time obtained by the space vacuum reflecting value ZPk to be T), calculating the cavity vacuum mean value at the adjacent time to obtain a second vacuum difference value, and adding and summing all the second vacuum difference values to obtain a time vacuum reflecting value ZPt.
And the processing module is used for calculating and processing to obtain the vacuum anomaly ratio based on the vacuum reflection value.
Wherein the vacuum anomaly ratio includes a spatial vacuum anomaly ratio Bk and a temporal vacuum anomaly ratio Bt.
In some embodiments, the monitoring platform: the spatial vacuum reflected value ZPk and the temporal vacuum reflected value ZPt are obtained, and the preset spatial vacuum reflected value ZPky and the preset temporal vacuum reflected value ZPty (preset spatial vacuum reflected value and preset temporal vacuum reflected value, which are set by a technician according to history data) are obtained.
By the formulaAnd calculating to obtain the space vacuum anomaly ratio Bk.
By the formulaAnd calculating to obtain the time vacuum anomaly ratio Bt.
The abnormality monitoring module comprises a monitoring sub-module and an abnormality judging sub-module.
And the monitoring sub-module is used for judging the working state of the curved surface vacuum laminating machine according to the abnormal vacuum ratio to obtain an operation state signal.
Wherein the operation state signal includes an operation normal signal and an operation abnormal signal.
In some embodiments, the spatial vacuum anomaly ratio Bk and the temporal vacuum anomaly ratio Bt are compared to corresponding spatial vacuum anomaly ratio thresholds and temporal vacuum anomaly ratio thresholds, respectively.
And if the space vacuum abnormality ratio Bk is smaller than the space vacuum abnormality ratio threshold, and the time vacuum abnormality ratio Bt is smaller than the time vacuum abnormality ratio threshold, generating a normal operation signal.
Otherwise (otherwise, the conditions comprise that the space vacuum anomaly ratio Bk is larger than or equal to the space vacuum anomaly ratio threshold value, the time vacuum anomaly ratio Bt is smaller than the time vacuum anomaly ratio threshold value, the space vacuum anomaly ratio Bk is smaller than the space vacuum anomaly ratio threshold value, the time vacuum anomaly ratio Bt is larger than or equal to the time vacuum anomaly ratio threshold value, the space vacuum anomaly ratio Bk is larger than or equal to the space vacuum anomaly ratio threshold value, and the time vacuum anomaly ratio Bt is larger than or equal to the time vacuum anomaly ratio threshold value), and an operation anomaly signal is generated.
It should be noted that, the abnormal operation signal indicates that the vacuum degree of the current curved surface vacuum laminating machine is respectively uneven in the vacuum cavity, the curved surface vacuum laminating machine breaks down in the operation process, the normal operation signal indicates that the vacuum degree of the current curved surface vacuum laminating machine is respectively even in the vacuum cavity, and the curved surface vacuum laminating machine does not break down in the operation process.
And the abnormality judging sub-module is used for further analyzing and judging according to the operation abnormality signals to obtain influence signals, wherein the influence signals comprise a space vacuum degree influence signal, a time vacuum degree influence signal and a mutual pre-influence signal.
In other embodiments, if the spatial vacuum anomaly ratio Bk is equal to or greater than the spatial vacuum anomaly ratio threshold, the temporal vacuum anomaly ratio Bt is less than the temporal vacuum anomaly ratio threshold, then a spatial vacuum degree influence signal is generated.
And if the space vacuum anomaly ratio Bk is smaller than the space vacuum anomaly ratio threshold value and the time vacuum anomaly ratio Bt is larger than or equal to the time vacuum anomaly ratio threshold value, generating a time vacuum degree influence signal.
And if the space vacuum anomaly ratio Bk is larger than or equal to the space vacuum anomaly ratio threshold value, and the time vacuum anomaly ratio Bt is larger than or equal to the time vacuum anomaly ratio threshold value, generating a mutual pre-influence signal.
The spatial vacuum degree influence signal indicates that: the space vacuum difference is caused, and the reasons for influencing the space vacuum difference are that the opening and closing positions of the vacuum cavity of the curved surface vacuum laminating machine are provided with leakage and the like, and the time vacuum degree influence signals indicate that: the time vacuum difference is caused by the time vacuum difference, and the reasons for influencing the time vacuum difference are the problems of leakage and the like at the opening and closing positions of the vacuum cavity of the curved surface vacuum laminating machine, the problems of a vacuum pump connected with the vacuum cavity and the like; the mutual pre-influence signal indicates that the spatial vacuum degree abnormality may influence the temporal vacuum degree abnormality.
The technical scheme of the embodiment of the invention comprises the following steps: the acquisition module is used for acquiring vacuum data of a vacuum cavity of the curved surface vacuum laminating machine; the analysis module is used for analyzing the curved surface vacuum laminating machine based on the vacuum data to obtain a vacuum reflection value; the processing module is used for calculating and processing to obtain a vacuum anomaly ratio based on the vacuum reflection value; the abnormality monitoring module comprises a monitoring sub-module and an abnormality judging sub-module; the monitoring submodule judges the working state of the curved surface vacuum laminating machine according to the vacuum abnormal ratio to obtain an operation state signal, and the abnormality judging submodule further analyzes and judges according to the operation abnormality signal; according to the embodiment of the invention, the working operation state of the curved surface vacuum laminating machine is judged by starting from the vacuum data of the vacuum cavity of the curved surface vacuum laminating machine and performing data processing analysis in the space and time dimensions, so that the real-time monitoring of the curved surface vacuum laminating machine is realized, and the laminating quality of the curved surface vacuum laminating machine is ensured; and further judging the abnormal reasons based on the operation abnormal signals to obtain the reasons for generating faults, so that the technical staff can conveniently overhaul the faults.
Example 2
Referring to fig. 3, based on the above embodiment 1, the present invention is a bonded vacuum anomaly monitoring system, further comprising:
and the confirmation module is used for evaluating and analyzing the faults of the curved surface vacuum laminating machine based on the mutual pre-influence signals.
In some embodiments, the validation module: when the mutual pre-influence signals are obtained, a two-dimensional coordinate system is constructed by taking the starting point of an analysis time period as an origin of an X axis, the duration of the analysis time period as the X axis and a vacuum difference value (the vacuum difference value comprises a first vacuum difference value and a second vacuum difference value) as a Y axis.
Substituting the first vacuum difference value and the second vacuum difference value into a two-dimensional coordinate system according to the duration of the corresponding analysis time period, and respectively drawing to obtain a first vacuum difference curve and a second vacuum difference curve;
all wave peak points and wave valley points in the first vacuum difference curve and the second vacuum difference curve are respectively obtained, marked as a first curve wave peak point, a first curve wave valley point, a second curve wave peak point and a second curve wave valley point, and the sum GBZ of the numbers of all wave peak points and wave valley points is counted.
And performing coincidence comparison on the first curve crest point and the second curve crest point, performing coincidence comparison on the first curve trough point and the second curve trough point, setting a coincidence deviation period, marking as a crest coincidence point if the time difference between the first curve crest point and the second curve crest point is within the coincidence deviation period, marking as a trough coincidence point if the time difference between the first curve trough point and the second curve trough point is within the coincidence deviation period, and counting the sum GBC of the numbers of all the crest coincidence points and the trough coincidence points.
By the formulaAnd calculating to obtain a resonance appearance value ZZB.
Comparing the obtained resonance appearance value ZZB with a resonance appearance threshold value;
if the resonance representation value ZZB is larger than or equal to the resonance representation threshold value, generating an opening and closing influence signal;
and if the resonance expression value ZZB is smaller than the resonance expression threshold value, generating a space vacuum degree and time vacuum degree composite influence signal.
The opening and closing influence signals show that the fluctuation frequency of the space vacuum abnormality is similar to that of the time vacuum abnormality, the space vacuum abnormality affects the time vacuum abnormality, and the reasons for affecting the space vacuum difference are that the opening and closing position of the vacuum cavity of the curved surface vacuum laminating machine has the problems of leakage and the like; the problems of leakage and the like at the opening and closing positions of the vacuum cavity of the curved surface vacuum laminating machine are required to be checked.
The composite influence signal of the space vacuum degree and the time vacuum degree indicates that the fluctuation frequency of the space vacuum abnormality and the time vacuum abnormality is far, and the influence of the space vacuum degree abnormality on the time vacuum degree abnormality is avoided.
The composite influence signal of the space vacuum degree and the time vacuum degree indicates the reason for causing the vacuum abnormality of the curved surface vacuum laminating machine, and the reasons for the influence signal of the time vacuum degree and the influence signal of the space vacuum degree exist, and the reasons for the influence signal of the space vacuum degree need to be checked for multiple times.
The technical scheme of the embodiment of the invention comprises the following steps: the confirming module is used for evaluating and analyzing faults of the curved surface vacuum laminating machine based on the mutual pre-influence signals; according to the embodiment of the invention, the vacuum difference value data of the analysis module is used for analyzing and judging whether the space and time anomalies have a mutual influence relationship or not through the coordinate model, so that the problem fault point can be conveniently and continuously found, and the checking and maintaining capacity of the curved surface vacuum laminating machine in the fault occurrence is further improved.
Example 3
Referring to fig. 4, based on the above embodiment 2, the present invention is a working method of a bonded vacuum anomaly monitoring system, comprising the following steps:
step 1: obtaining vacuum data of a vacuum cavity of the curved surface vacuum laminating machine; the vacuum data comprise an upper cavity vacuum value, a lower cavity vacuum value and an opening and closing position vacuum value;
the specific implementation process of the step 1 is as follows: thereby obtaining the upper cavity vacuum value and the lower cavity vacuum value; and each side surface of the opening and closing positions of the two sealing cover plates is respectively provided with a vacuum sensor, so that a plurality of vacuum values of the opening and closing positions are obtained.
Step 2: analyzing the curved surface vacuum laminating machine based on the vacuum data to obtain a vacuum reflection value; wherein the vacuum reflected values include a spatial vacuum reflected value ZPk and a temporal vacuum reflected value ZPt;
the specific implementation process of the step 2 is as follows: firstly, obtaining a plurality of opening and closing vacuum values, and adding the plurality of opening and closing vacuum values to obtain an average value, so as to obtain an opening and closing vacuum average value; then, respectively carrying out difference calculation on the vacuum average value at the opening and closing position and the upper cavity vacuum value and the lower cavity vacuum value to obtain an upper vacuum difference value and a lower vacuum difference value; meanwhile, calculating the difference value of the vacuum values of the adjacent opening and closing positions to obtain a first vacuum difference value, and adding and summing all the first vacuum difference values to obtain an opening and closing vacuum difference value; and adding and summing the obtained upper vacuum difference value, lower vacuum difference value and opening and closing vacuum difference value to obtain a space vacuum reflection value ZPk.
Adding and summing the upper cavity vacuum value, the lower cavity vacuum value and the vacuum average value at the opening and closing position to obtain an average value, and obtaining a cavity vacuum average value; setting an analysis time period (the time length value of the analysis time period is T, the end point of the analysis time period is the time obtained by the space vacuum reflecting value ZPk, the time length value of the analysis time period is subtracted from the time obtained by the space vacuum reflecting value ZPk to be T), calculating the cavity vacuum mean value at the adjacent time to obtain a second vacuum difference value, and adding and summing all the second vacuum difference values to obtain a time vacuum reflecting value ZPt.
Step 3: the processing module is used for calculating and processing to obtain a vacuum anomaly ratio based on the vacuum reflection value; wherein the vacuum anomaly ratio comprises a space vacuum anomaly ratio Bk and a time vacuum anomaly ratio Bt;
the specific implementation process of the step 3 is as follows: acquiring a space vacuum reflected value ZPk and a time vacuum reflected value ZPt, and acquiring a preset space vacuum reflected value ZPky and a preset time vacuum reflected value ZPty; by the formulaCalculating to obtain a space vacuum anomaly ratio Bk; by the formula->And calculating to obtain the time vacuum anomaly ratio Bt.
Step 4: the monitoring submodule is used for judging the working state of the curved surface vacuum laminating machine according to the vacuum abnormal ratio to obtain an operation state signal; wherein the operation state signal includes an operation normal signal and an operation abnormal signal; according to the abnormal operation signal, further analyzing and judging to obtain an influence signal;
the specific implementation process of the step 4 is as follows: comparing the space vacuum anomaly ratio Bk and the time vacuum anomaly ratio Bt with corresponding space vacuum anomaly ratio threshold and time vacuum anomaly ratio threshold respectively;
if the space vacuum anomaly ratio Bk is smaller than the space vacuum anomaly ratio threshold value, generating a normal operation signal when the time vacuum anomaly ratio Bt is smaller than the time vacuum anomaly ratio threshold value; otherwise, an operation anomaly signal is generated.
If the obtained space vacuum anomaly ratio Bk is larger than or equal to a space vacuum anomaly ratio threshold value, and the time vacuum anomaly ratio Bt is smaller than a time vacuum anomaly ratio threshold value, generating a space vacuum degree influence signal; the space vacuum anomaly ratio Bk is smaller than a space vacuum anomaly ratio threshold value, and the time vacuum anomaly ratio Bt is larger than or equal to a time vacuum anomaly ratio threshold value, so that a time vacuum degree influence signal is generated; and if the space vacuum anomaly ratio Bk is larger than or equal to the space vacuum anomaly ratio threshold value, and the time vacuum anomaly ratio Bt is larger than or equal to the time vacuum anomaly ratio threshold value, generating a mutual pre-influence signal.
Step 5: based on the mutual pre-influence signals, evaluating and analyzing faults of the curved surface vacuum laminating machine;
the specific implementation process of the step 5 is as follows: when the mutual pre-influence signals are obtained, a two-dimensional coordinate system is constructed by taking the starting point of an analysis time period as an origin of an X axis, the duration of the analysis time period as the X axis and a vacuum difference value (the vacuum difference value comprises a first vacuum difference value and a second vacuum difference value) as a Y axis.
Substituting the first vacuum difference value and the second vacuum difference value into a two-dimensional coordinate system according to the duration of the corresponding analysis time period, and respectively drawing to obtain a first vacuum difference curve and a second vacuum difference curve; all wave peak points and wave valley points in the first vacuum difference curve and the second vacuum difference curve are respectively obtained, marked as a first curve wave peak point, a first curve wave valley point, a second curve wave peak point and a second curve wave valley point, and the sum GBZ of the numbers of all wave peak points and wave valley points is counted.
And performing coincidence comparison on the first curve crest point and the second curve crest point, performing coincidence comparison on the first curve trough point and the second curve trough point, setting a coincidence deviation period, marking as a crest coincidence point if the time difference between the first curve crest point and the second curve crest point is within the coincidence deviation period, marking as a trough coincidence point if the time difference between the first curve trough point and the second curve trough point is within the coincidence deviation period, and counting the sum GBC of the numbers of all the crest coincidence points and the trough coincidence points.
By the formulaAnd calculating to obtain a resonance appearance value ZZB.
Comparing the obtained resonance appearance value ZZB with a resonance appearance threshold value; if the resonance representation value ZZB is larger than or equal to the resonance representation threshold value, generating an opening and closing influence signal; if the resonance expression value ZZB is smaller than the resonance expression threshold value, generating a space vacuum degree and time vacuum degree composite influence signal;
the technical scheme of the implementation is as follows: the working operation state of the curved surface vacuum laminating machine is judged by starting from the vacuum data of the vacuum cavity of the curved surface vacuum laminating machine and performing data processing analysis in the space and time dimensions, so that the curved surface vacuum laminating machine is monitored in real time, and the laminating working quality of the curved surface vacuum laminating machine is ensured; and based on the operation abnormal signal, further judging the abnormal reason to obtain the reason for generating the fault, thereby facilitating the maintenance of technicians; the vacuum difference data is also utilized to analyze and judge whether the space and time anomalies have a mutual influence relationship or not through a coordinate model, so that the problem fault point can be conveniently and continuously found, and the checking and maintaining capacity of the curved surface vacuum laminating machine in the fault state is further improved; therefore, the vacuum degree in the bonding process is monitored and judged in real time, the bonding process abnormality can be judged based on the judging signals, and the fault problem causing the vacuum abnormality can be found out through multiple analysis and judgment.
The above formulas are all formulas with dimensions removed and numerical values calculated, the formulas are formulas with a large amount of data collected for software simulation to obtain the latest real situation, and preset parameters in the formulas are set by those skilled in the art according to the actual situation.
The foregoing describes one embodiment of the present invention in detail, but the description is only a preferred embodiment of the present invention and should not be construed as limiting the scope of the invention. All equivalent changes and modifications within the scope of the present invention are intended to be covered by the present invention.
Claims (10)
1. A conformable vacuum anomaly monitoring system, comprising:
the acquisition module is used for acquiring vacuum data of a vacuum cavity of the curved surface vacuum laminating machine; the vacuum data comprise an upper cavity vacuum value, a lower cavity vacuum value and an opening and closing position vacuum value;
the analysis module is used for analyzing the curved surface vacuum laminating machine based on the vacuum data to obtain a vacuum reflection value; wherein the vacuum reflected values include a spatial vacuum reflected value ZPk and a temporal vacuum reflected value ZPt;
the processing module is used for calculating and processing to obtain a vacuum anomaly ratio based on the vacuum reflection value; wherein the vacuum anomaly ratio comprises a space vacuum anomaly ratio Bk and a time vacuum anomaly ratio Bt; the space vacuum anomaly ratio Bk passes through a formulaCalculated, the time vacuum abnormality ratio Bt is calculated by the formula +.>Calculating to obtain ZPky as a preset space vacuum reflecting value and ZPty as a preset time vacuum reflecting value;
the abnormality monitoring module comprises a monitoring sub-module and an abnormality judging sub-module; the monitoring submodule is used for judging the working state of the curved surface vacuum laminating machine according to the vacuum abnormal ratio to obtain an operation state signal; wherein the operation state signal includes an operation normal signal and an operation abnormal signal;
and the abnormality judging sub-module is used for generating a mutual pre-influence signal if the space vacuum abnormality ratio Bk is larger than or equal to the space vacuum abnormality ratio threshold value and the time vacuum abnormality ratio Bt is larger than or equal to the time vacuum abnormality ratio threshold value based on the operation abnormality signal.
2. The bonded vacuum anomaly monitoring system of claim 1, wherein the spatial vacuum reflection value is obtained by:
firstly, obtaining a plurality of opening and closing vacuum values, and adding the plurality of opening and closing vacuum values to obtain an average value, so as to obtain an opening and closing vacuum average value;
then, respectively carrying out difference calculation on the vacuum average value at the opening and closing position and the upper cavity vacuum value and the lower cavity vacuum value to obtain an upper vacuum difference value and a lower vacuum difference value;
meanwhile, calculating the difference value of the vacuum values of the adjacent opening and closing positions to obtain a first vacuum difference value, and adding and summing all the first vacuum difference values to obtain an opening and closing vacuum difference value;
and adding and summing the obtained upper vacuum difference value, lower vacuum difference value and opening and closing vacuum difference value to obtain a space vacuum reflection value ZPk.
3. The bonded vacuum anomaly monitoring system of claim 2, wherein the time vacuum reflected value is obtained by the following steps:
adding and summing the upper cavity vacuum value, the lower cavity vacuum value and the vacuum average value at the opening and closing position to obtain an average value, and obtaining a cavity vacuum average value;
setting an analysis time period, performing difference calculation on the cavity vacuum average values at adjacent moments to obtain second vacuum difference values, and adding and summing all the second vacuum difference values to obtain a time vacuum reflection value ZPt.
4. A conformable vacuum anomaly monitoring system according to claim 3, wherein the duration of the analysis time period is T, the end of the analysis time period is the time at which the spatial vacuum reflected value ZPk was obtained, and the start of the analysis period is the time at which the spatial vacuum reflected value ZPk was obtained minus the duration of the analysis time period is T.
5. The bonded vacuum anomaly monitoring system of claim 4, wherein if the spatial vacuum anomaly ratio Bk is less than the spatial vacuum anomaly ratio threshold, the temporal vacuum anomaly ratio Bt is less than the temporal vacuum anomaly ratio threshold, generating a normal operation signal;
otherwise, an operation anomaly signal is generated.
6. The bonded vacuum anomaly monitoring system of claim 5, wherein if the obtained spatial vacuum anomaly ratio Bk is equal to or greater than a spatial vacuum anomaly ratio threshold, the temporal vacuum anomaly ratio Bt is less than the temporal vacuum anomaly ratio threshold, generating a spatial vacuum degree influence signal;
if the obtained space vacuum anomaly ratio Bk is smaller than the space vacuum anomaly ratio threshold value, and the time vacuum anomaly ratio Bt is larger than or equal to the time vacuum anomaly ratio threshold value, generating a time vacuum degree influence signal.
7. The conformable vacuum anomaly monitoring system of claim 1, further comprising:
and a confirmation module: when the mutual pre-influence signals are obtained, a two-dimensional coordinate system is constructed by taking the starting point of an analysis time period as the origin of an X axis, the duration of the analysis time period as the X axis and the vacuum difference as the Y axis;
substituting the first vacuum difference value and the second vacuum difference value into a two-dimensional coordinate system according to the duration of the corresponding analysis time period, and respectively drawing to obtain a first vacuum difference curve and a second vacuum difference curve;
respectively obtaining all peak points and trough points in a first vacuum difference curve and a second vacuum difference curve, marking the peak points and the trough points as a first curve peak point, a first curve trough point, a second curve peak point and a second curve trough point, and counting the sum GBZ of the numbers of all the peak points and the trough points;
the first curve crest point and the second curve crest point are subjected to coincidence comparison, the first curve trough point and the second curve trough point are subjected to coincidence comparison, a coincidence deviation period is set, if the time difference value between the first curve crest point and the second curve crest point is within the coincidence deviation period, the first curve crest point and the second curve trough point are marked as crest coincidence points, and if the time difference value between the first curve trough point and the second curve trough point is within the coincidence deviation period, the first curve trough point and the second curve trough point are marked as trough coincidence points, and the sum GBC of the numbers of all the crest coincidence points and the trough coincidence points is counted;
by the formulaAnd calculating to obtain a resonance appearance value ZZB.
8. The conformable vacuum anomaly monitoring system of claim 7 wherein the vacuum differential comprises a first vacuum differential and a second vacuum differential.
9. The bonded vacuum anomaly monitoring system of claim 8, wherein the open/close influencing signal is generated if the resonance performance value zzzb is greater than or equal to a resonance performance threshold.
10. The bonded vacuum anomaly monitoring system of claim 9, wherein if the resonance performance value zzzb is less than the resonance performance threshold, a spatial vacuum level and temporal vacuum level composite impact signal is generated.
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2012073300A (en) * | 2010-09-27 | 2012-04-12 | Shibaura Mechatronics Corp | Substrate lamination device and substrate lamination method |
| CN103325738A (en) * | 2012-03-23 | 2013-09-25 | 芝浦机械电子装置股份有限公司 | Substrate attachment device and substrate attachment method |
| JP2015055853A (en) * | 2013-09-13 | 2015-03-23 | 信越エンジニアリング株式会社 | Device and method for manufacturing laminated device |
| CN107009718A (en) * | 2017-05-10 | 2017-08-04 | 东莞市万丰纳米材料有限公司 | Abutted equipment in bend glass vacuum |
| CN110825003A (en) * | 2019-12-02 | 2020-02-21 | 安徽泰臻真空科技有限公司 | Vacuum equipment monitoring control system |
| CN115390513A (en) * | 2022-07-14 | 2022-11-25 | 深圳市博硕科技股份有限公司 | Remote intelligent monitoring system of automatic laminating machine |
-
2023
- 2023-12-11 CN CN202311692910.6A patent/CN117390572B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2012073300A (en) * | 2010-09-27 | 2012-04-12 | Shibaura Mechatronics Corp | Substrate lamination device and substrate lamination method |
| CN103325738A (en) * | 2012-03-23 | 2013-09-25 | 芝浦机械电子装置股份有限公司 | Substrate attachment device and substrate attachment method |
| JP2015055853A (en) * | 2013-09-13 | 2015-03-23 | 信越エンジニアリング株式会社 | Device and method for manufacturing laminated device |
| CN107009718A (en) * | 2017-05-10 | 2017-08-04 | 东莞市万丰纳米材料有限公司 | Abutted equipment in bend glass vacuum |
| CN110825003A (en) * | 2019-12-02 | 2020-02-21 | 安徽泰臻真空科技有限公司 | Vacuum equipment monitoring control system |
| CN115390513A (en) * | 2022-07-14 | 2022-11-25 | 深圳市博硕科技股份有限公司 | Remote intelligent monitoring system of automatic laminating machine |
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